基于问卷的人工智能用于感染性疾病临床决策支持：越南中部一家医院的试点研究（Table_2，DOCX）

数据集获取（Figshare）

• 源站标题：Table_2_Development and application of survey-based artificial intelligence for clinical decision support in managing infectious diseases: A pilot study on a hospital in central Vietnam.DOCX
• DOI：10.3389/fpubh.2022.1023098.s003
• 发布日期：2022-11-09
• 文件数：1 个
• 文件总体积：80.56 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Development_and_application_of_survey-based_artificial_intelligence_for_clinical_decision_support_in_managing_infectious_diseases_A_pilot_study_on_a_hospital_in_central_Vietnam_DOCX/21526308

命令行下载

curl -L -o "Table_2_Development and application of survey-based artificial intelligence for clinical decision support in managing infectious diseases: A pilot stu.DOCX" "https://ndownloader.figshare.com/files/38158056"

文件清单

• Table_2_Development and application of survey-based artificial intelligence for clinical decision support in managing infectious diseases: A pilot stu.DOCX（80.56 KB）

文件总体积 80.56 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

IntroductionIn this study, we developed a simplified artificial intelligence to support the clinical decision-making of medical personnel in a resource-limited setting.
MethodsWe selected seven infectious disease categories that impose a heavy disease burden in the central Vietnam region: mosquito-borne disease, acute gastroenteritis, respiratory tract infection, pulmonary tuberculosis, sepsis, primary nervous system infection, and viral hepatitis. We developed a set of questionnaires to collect information on the current symptoms and history of patients suspected to have infectious diseases. We used data collected from 1,129 patients to develop and test a diagnostic model. We used XGBoost, LightGBM, and CatBoost algorithms to create artificial intelligence for clinical decision support. We used a 4-fold cross-validation method to validate the artificial intelligence model. After 4-fold cross-validation, we tested artificial intelligence models on a separate test dataset and estimated diagnostic accuracy for each model.
ResultsWe recruited 1,129 patients for final analyses. Artificial intelligence developed by the CatBoost algorithm showed the best performance, with 87.61% accuracy and an F1-score of 87.71. The F1-score of the CatBoost model by disease entity ranged from 0.80 to 0.97. Diagnostic accuracy was the lowest for sepsis and the highest for central nervous system infection.
ConclusionSimplified artificial intelligence could be helpful in clinical decision support in settings with limited resources.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpubh.2022.1023098.s003（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Development_and_application_of_survey-based_artificial_intelligence_for_clinical_decision_support_in_managing_infectious_diseases_A_pilot_study_on_a_hospital_in_central_Vietnam_DOCX/21526308）。